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|Title:||Medical image/ video analysis for therapeutic ingestible microcapsule||Authors:||Tan, Ping Chun.||Keywords:||DRNTU::Engineering||Issue Date:||2010||Abstract:||This report presents a study of multi-level local feature classification for bleeding detection in Wireless Capsule Endoscopy (WCE) images using MATLAB. The image feature that is used in classification is color. There are 3 levels of classification: low-level, intermediate-level and high-level classification. In low-level classification, each cell of N by N pixels is characterized by adaptive color histogram which is used as feature representation for WCE images. A Neural Network (NN) cell-classifier is trained to classify cells in an image as bleeding or non-bleeding patches. In the intermediate-level classification, a block which covers 3 by 3 cells is formed. The intermediate-level representation of the block is generated from the low-level classifications of the cells, which captures the spatial local correlations of the cell classifications. Again, a NN block classifier is trained to classify the blocks as bleeding or non-bleeding ones. In high-level classification, the low-level and intermediate-level classifications are used by decision making rule to make a final decision. Experiments on clinical WCE videos have shown that the method of classification is not only accurate in both detection and differentiating bleeding or non-bleeding in WCE images.||URI:||http://hdl.handle.net/10356/40201||Rights:||Nanyang Technological University||Fulltext Permission:||restricted||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Student Reports (FYP/IA/PA/PI)|
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